A profit-driven Artificial Neural Network (ANN) with applications to fraud detection and direct marketing

The rapid growth in data capture and computational power has led to an increasing focus on data-driven research. So far, most of the research is focused on predictive modeling using statistical optimization, while profit maximization has been given less priority. It is exactly this gap that will be addressed in this study by taking a profit-driven approach to develop a profit-driven Artificial Neural Network (ANN) classification technique. In order to do this, we have first introduced an ANN model with a new penalty function which gives variable penalties to the misclassification of instances considering their individual importance (profit of correctly classification and/or cost of misclassification) and then we have considered maximizing the total net profit. In order to generate individual penalties, we have modified the sum of squared errors (SSE) function by changing its values with respect to profit of each instance. We have implemented different versions of ANN of which five of them are new ones contributed in this study and two benchmarks from relevant literature. We appraise the effectiveness of the proposed models on two real-life data sets from fraud detection and a University of California Irvine (UCI) repository data set about bank direct marketing. For the comparison, we have considered both statistical and profit-driven performance metrics. Empirical results revealed that, although in most cases the statistical performance of new models are not better than previous ones, they turn out to be better when profit is the concern.